In general, radiation therapy or radiotherapy, uses a beam of ionizing radiation to treat living tissue, usually a tumor. As used herein, the term radiotherapy should be broadly construed and is intended to include various techniques used to irradiate a patient, including use of either photons (such as high energy x-rays and gamma rays) or particles (such as electron and proton beams), and applies to both therapeutic and radiosurgical applications. For purposes of the present invention, the processes of treatment planning and administering the radiation to a patient can be generalized regardless of the type of radiation used.
Modern radiation therapy techniques include Intensity Modulated Radiotherapy (“IMRT”), volumetric modulated arc therapy (where the system gantry moves while radiation is delivered) and three-dimensional conformal radiotherapy (“3D conformal” or “3DCRT”). These techniques are typically implemented using a radiotherapy system, such as a linear accelerator, equipped with a multileaf collimator (“MLC”). While modern linear accelerators use MLCs, other methods of providing conformal radiation to a target volume are known and are within the scope of the present invention. Use of multileaf collimators in general, and arc therapy and IMRT techniques in particular, allow the radiologist to treat a patient from multiple angles while varying the shape and dose of the radiation beam, thereby providing greatly enhanced ability to deliver radiation to a target within a treatment volume while avoiding excess irradiation of nearby healthy tissue. The greater freedom which IMRT, arc therapy and other complex radiotherapy techniques provide has made the task of developing treatment plans more difficult.
Treatment planning typically starts with (1) images of the treatment volume (e.g., from CT or MRI scans) and, (2) the desired dose of radiation which is to be delivered to various portions of a target, such as a tumor, and (3) the maximum dose which can be safely absorbed by tissue structures, such as organs, within the treatment volume that are adjacent to or near the tumor or other target volume. As used herein, the term “treatment volume” is used to refer to the entire volume that will be subjected to radiation, and is sometimes referred to as the “irradiated volume.” The target volume is intended to receive a therapeutic prescribed dose, and is sometimes referred to as the “planning target volume” (“PTV”). Thus, the target volume is within the treatment volume. Both the target volume and any nearby organs within the treatment volume may have complex three dimensional shapes compounding the difficulty of preparing a treatment plan.
The patient specific treatment planning information (e.g., volume boundaries, desired dose, etc.) is used to define or determine an objective function (sometimes referred to as a “cost function”) that is then used in the treatment planning process. Thus, the typical objective function incorporates patient specific information comprising a combination of empirical data and prescribed dose information and limitations. More specifically, the objective function typically contains what are referred to as dose volume histogram (“DVH”) constraints. The DVH constraints define both how much radiation is required in the various portions of the target volume, as well as limits on radiation in the remaining portions of the treatment volume outside the target volume. For example, a DVH constraint may specify that a certain structure not receive more than A dose in B % of the structure's volume; or it may specify that a tumor should receive at least x dose in y % of the tumor volume. There may be many DVH constraints in the objective function. The objective function may reflect tradeoffs arising due to the need to adequately irradiate a tumor, on one hand, and to protect surrounding tissue, on the other. The proper tradeoff or balance between these competing goals may not be clear at the outset of the process. Likewise, it may not be clear at the outset whether or how the treatment planning software can accommodate competing goals.
A variety of optimization algorithms have been developed to use the objective function to solve the “inverse problem” of devising and optimizing a specific, three-dimensional treatment plan for irradiating the treatment volume from a variety of angles (or, in arc therapy, while the system gantry is moving), in order to deliver a desired radiation dose to the target while minimizing irradiation of nearby tissue. The treatment plan preferably also takes into account the capabilities and physical limitations of the radiotherapy system to be used. Generally speaking, the inverse problem involves optimizing the selection of angles, the selection of MLC leaf movements and the durations of irradiations in accordance with the constraints of the objective function. Because of the large number of variables involved and complex matrix manipulations that are required, the optimization algorithms for calculating treatment plans require substantial computational time even when using modern high speed computers. These problems are even more difficult in treatment planning for arc therapy, which uses a moving source of radiation.
Generally two types of algorithms are used in treatment planning: (1) dose calculations algorithms based on a given set system parameters, e.g., gantry angle, MLC leaf positions, etc., and (2) search algorithms which use various techniques to adjust system parameters between dose calculations to achieve optimization of the plan. Some exemplary dose calculation algorithms include various Monte Carlo (“MC”) techniques and pencil beam convolution (“PBC”). Some exemplary search algorithms include various stochastic and deterministic methods, including various simulated annealing (“SA”) techniques, algebraic inverse treatment planning (“AITP”), and simultaneous iterative inverse treatment planning (“SIITP”). Such techniques, and others, are well known in the art, and each of the techniques has advantages and disadvantages relative to the others. Each of the methods requires iterative dose calculations for optimization, and generally a high number of dose calculation iterations or “passes” are required to converge on an optimal plan. Typically, each iteration involves changing the boundary conditions using the search algorithm and recalculating the dose distribution. While a fully optimized plan might be achieved using known methods if adequate time is available, as a practical matter time constraints often limit the ability to achieve this goal.
It is noted that a treatment plan is typically implemented over a time period. Thus, the patient typically is given multiple treatments over the course of days or weeks, such that the dose delivered to the treatment volume is fractionated. During the time between treatments changes may occur in the treatment volume, for example, the tumor being irradiated may shrink in size or surrounding organs may change position. Any such changes may necessitate revising and re-optimizing the treatment plan before the next fractionated dose or “fraction” is delivered. The problem of re-optimizing a treatment plan is known, and presents somewhat different issues than achieving an initially optimized plan as described herein. Since the use of fractions does not otherwise affect the treatment planning process, it is not necessary to discuss it in further detail.
Treatment planning algorithms may be implemented as part of an overall, integrated treatment planning software package which provides additional features and capabilities. For example, a dose calculation algorithm and search algorithm may be used to optimize a set of fluence maps at each gantry angle, with a separate leaf sequencer used to calculate the leaf movements needed to deliver them. Alternatively, a dose calculation algorithm and search algorithm may be used to directly optimize leaf movements and other machine parameters. The Eclipse™ Treatment Planning System offered by the assignee of the present invention includes such an integrated software program.